The present invention relates to techniques for analyzing a body of data. More specifically, the invention relates to techniques that analyze an image by finding near neighbors of pixels in the image.
Burr, D. J., "A Dynamic Model for Image Registration," Computer Graphics and Image Processing,"15, 1981, pp. 102-112, describes an iterative technique for updating the local registration of two images. The introduction indicates that nearest neighbor correspondences become better registration indicators as misregistration decreases. Section 3 on pages 104-106 describes the use of a sum of nearest neighbor edge element distances as a spatial distance measure. FIG. 1 shows vectors indicating nearest neighbors for two overlaid images.
Ullman, S., "An Approach to Object Recognition: Aligning Pictorial Descriptions," A.I. Memo No. 931, December 1986, M.I.T. Artificial Intelligence Laboratory, Cambridge, Mass., describes a two stage recognition process. The first stage determines the transformation in space that is necessary to bring the viewed object into alignment with possible object-models. The second stage determines the model that best matches the viewed object. Page 7 describes a typical similarity measure used in associative memories called the Hamming distance, defined for two binary vectors as the number of coordinates in which they disagree. Section 4, beginning on page 20, describes an alignment approach that searches a space of all stored object-models and all of their possible views for a particular model and a particular transformation that maximizes some measure of fit between the object and the model. Page 48 discusses general requirements of measures of the degree of match.